Toolbox for Regional Policy Analysis Report (2000)

Case Study: Tren Urbano

Overview

This case study illustrates the measurement of regional accessibility to employment. Accessibility is measured by mode of transportation and by income group. The method can be applied using census and employment data, in conjunction with highway and transit network data from a regional travel demand model.

Working in partnership with local planning officials, the Tren Urbano Project at the Massachusetts Institute of Technology (MIT) evaluated the regional accessibility and equity impacts of a proposed rail transit system in San Juan, Puerto Rico. The impacts of the rail system were compared with the impacts of a "no-build" alternative, other transportation improvements, and land use policies to concentrate development near transit stations. The calculations were performed using a geographic information system (GIS), which permitted the graphical display of existing accessibility levels and changes in accessibility, as well as the analysis of impacts across income groups.

The study found that while the rail system will have accessibility benefits, these benefits are highly localized along the rail alignment and may not decrease the inequities in mobility among income groups. The study further found that policies to increase development in station areas would increase the accessibility benefits of the system, with these benefits distributed across all income groups.

In addition to analyzing accessibility at a regional level, the team used GIS tools to evaluate the walkability of station areas along the proposed system. A quareter- to half-mile radius is typically considered as the walking service area for a rail station. A more detailed analysis, however, shows that the actual area that can be reached by pedestrians may be much smaller than the area of the circle if direct streets or pathways are not provided (Figure 1).

Figure 1. Quarter-Mile Walking Distance, Tren Urbano Stations

Source: Zhang, Shen, and Sussman (1998).

The regional and station-area accessibility analyses were conducted using commonly available transportation data sets and GIS analytical tools. The results suggest transportation and land use strategies that might be implemented to reduce spatial inequity and to enhance both transit and employment accessibility.

Context

The San Juan metropolitan area of Puerto Rico encompasses a population of 1.3 million. A densely developed urban environment, high levels of congestion, limited opportunities for highway capacity expansion, and inadequate public transit service led the region to consider construction of a 12-mile "Tren Urbano" heavy-rail line. This project would provide high-speed, high-quality transit service connecting major employment, residential, and activity centers in the region (Figure 2).

Figure 2. San Juan Metropolitan Region

Source: Zhang, Shen, and Sussman (1998).

The San Juan Metropolitan Region has a radial road framework with jobs highly concentrated at the core. Population is more highly concentrated in the central area than in a typical U.S. city. The level of automobile ownership in San Juan is relatively high, but for those without cars, two general transit alternatives are available: buses and Publicos (Publicos are private, fixed-route, shared-ride car, minibus, or van services). The service patterns of each service vary. Public bus service is primarily concentrated in a north-south corridor of higher density in the city of San Juan and neighboring areas. Publico service is somewhat more dispersed as a result of its demand-responsive nature; there are Publico terminals in different locations and a more east-west pattern of service is visible. The Tren Urbano rail system would run primarily east-west, turning to the north at its east end to serve the highest-density corridor in the region.

Methodology

Regional Accessibility

Description

To compare the various project alternatives, the authors calculated a measure of accessibility of population to employment opportunities. The accessibility measure selected is a gravity-model-based measure known as the Hansen Model. This is formulated as:

Where Ai is accessibility from zone i to the employment opportunities in the San Juan Metropolitan Region; Oj is opportunities (total employment) in zone j; and Cij is the travel time for a trip from zone i to j. f(Cij) is an impedance function, which is adopted from the San Juan Regional Transportation Plan (SJRTP):

where a, b and gare model coefficients, given by SJRTP. Cij is the travel time from zone i to zone j, and e is the base of the natural logarithms. The constant a is set to one for convenience; since accessibility is a relative measure in this case, doing so will not affect the results.

To determine an overall weighted accessibility score for a set of zones, A, the measure for each zone is multiplied by the population of that zone. This product is then summed across all zones for which accessibility is to be calculated. The result is divided by total population in this set of zones:

While this measure is unit-free and has no intrinsic meaning, it provides an indication of the level of accessibility of jobs for each zone, with jobs weighted more heavily the closer they are (in terms of travel time) to the zone.

Scope

The research team calculated this accessibility measure for the following groups:

By mode, based on the network travel times for each mode. Three distinct public transit modes exist in San Juan: bus, Publico, and rail (under the Tren Urbano build alternative). These were also combined into an overall public transit accessibility measure by selecting the shortest zone-to-zone travel times of the three modes.

By income group, for five income groups. This was done based on the median income level for each zone. Zones were divided into five groups, depending on which income range they fell into. Then, the weighted accessibility index as shown above was determined for each group of zones.

In addition to absolute accessibility, relative accessibility was also compared among modes and income groups. This was done by dividing the group's accessibility score by the regional average accessibility score.

Computation

The data sets used in the study include:

Origin-destination tables for inter-zonal travel time and travel costs by modes and by types of trips (1990 Base Year and 2010 estimates);

Demographic and socioeconomic information, including total population and median income, from the 1990 Census; and

Employment by TAZ distribution in four sectors - basic, retail, service, and government; and

TAZ geographic boundary files.

C programming was used to compute accessibility scores based on travel model output. These scores were then imported into ArcInfo/ArcView GIS to produce tables and maps.

The entire process of designing the Tren Urbano analysis, preparing data, writing programs, analyzing data, and reporting on results was conducted by a research assistant over a two-year period. The project team estimates that once the data and programs have been prepared, doing the calculations and producing tables and maps could take less than a day for the experienced analyst. In each run of the analysis, however, a significant amount of time is taken up by importing and exporting data to and from the GIS package. The project team believes that these procedures could be automated by someone knowledgeable in programming and GIS. This might take a couple of months of staff time but would save significant effort in the long run, if the analyses are to be repeated.

Station-Area Walkability

In addition to measuring regional accessibility, the MIT research team also evaluated station-area walkability for the Tren Urbano system. People are typically willing to walk no more than a quarter- to a half-mile to access a rail transit system, and the benefits largely accrue to land uses located within this area. If the street network is circuitous or other barriers to travel exist, the accessible land area may be smaller than the area within a given radius, and the number of uses that benefit may be reduced.

To describe station-area walkability, the authors developed a "Walkability Index" (WI) that is defined as the ratio of the area of the observed transit impact zone (e.g., the land within an actual quarter-mile walking distance) to the area of the expected impact zone (e.g., a circle of quarter-mile radius). For a station that has no pedestrian access, the index is zero; for perfect pedestrian access (e.g., open land where the pedestrian can walk anywhere), the value is one. For a standard grid system, the index value is 0.6.

The authors measured the walkability of each station area using Census TIGER/Line files and GIS analysis tools. While the TIGER/Line files do not contain every piece of information relevant to walkability (e.g., sidewalks or pedestrian bridges), they do represent well the physical layout of the street network. Some cities or metropolitan areas have developed their own GIS file of the street network, which could also be used for this analysis.

To conduct the walkability analysis, a GIS package with network analysis capabilities, such as ArcInfo or TransCad, is required. The GIS is used to trace the street network within a quarter- to half-mile network distance of the station. Then, a buffer is drawn around these lines to represent the land area that is reachable within this walking distance. The buffer can be selected based on local characteristics. For example, in the Tren Urbano analysis area, a typical city block is 100 meters deep, so a buffer depth of 50 meters, or half this distance, was selected.

Application

Regional Accessibility

Baseline Accessibility

The study authors first examined existing 1990 accessibility levels. The automobile mode provides by far the highest regional accessibility scores. The accessibility pattern illustrates the radial nature of the road system, with jobs highly concentrated at the core, as accessibility declines in a ring-belt pattern away from the core area (Figure 3).

Figure 3. Employment Accessibility by Automobile

Source: Zhang, Shen, and Sussman (1998).

Accessibility levels for transit are generally much lower, and show varying patterns depending on the system. Bus accessibility, for example, is greatest along a north-south corridor that represents the core service area (Figure 4). Publico service is generally provided outside of bus service areas. The difference in accessibility scores between the different modes represents the difference in travel impedance, since the spatial distribution of employment and population is fixed.

Figure 4. Employment Accessibility by Bus

Source: Zhang, Shen, and Sussman (1998).

Table 1 shows how the auto and transit accessibility scores vary across income groups. For the auto mode, the high income areas have greater accessibility than low-income areas. For transit, higher accessibility scores are observed at both ends of the spectrum, with the highest accessibility at the high end of the income spectrum. This indicates that both low-income and high-income populations tend to be located in areas well-served by transit.

Table 1.
1990 Base Case: Population-Weighted Average Accessibility Scores and Indices by Income Group

High
(>$12.5K)

Med. High
($10-$12.5K)

Medium
($7.5-$10K)

Med. Low
($5-$7.5K)

Low
(<$5K)

Total
Region

Mode

Scores

Index

Scores

Index

Scores

Index

Scores

Index

Scores

Index

Scores

Index

Auto

5,450

1.45

3,809

1.01

3,347

0.89

3,150

0.84

3,624

0.96

3,763

1.00

Combined
Transit

1,101

1.43

761

0.99

672

0.87

755

0.98

903

1.17

770

1.00

Tren Urbano Impacts

The next step was to compare employment accessibility under the 2010 Tren Urbano Build and No-Build cases. Figure 5 compares the auto and combined public transit accessibility indices for each case. Construction of Tren Urbano, along with related transit service improvements, increases the regional transit accessibility score from 770 to 999, an increase of 30 percent. Examining results by income level, all income groups are better off in the Tren Urbano Build Case but worse off in the No-Build Case, compared to the 1990 base. Examining the relative accessibility indices by income group, the higher-income zones appear to gain somewhat more on average than the lower-income zones. Figure 6 shows the change in accessibility by zone for the Build versus No-Build alternatives. (Note that this analysis does not account for potential redistribution of income groups in the future, or for actual ridership by income group.)

Figure 5. 2010 Population-Weighted Average Accessibility

Source: Zhang, Shen, and Sussman (1998).

Thus, the authors conclude, the rail system will have accessibility benefits for the region. However, these benefits will be highly localized along the rail alignment, and will not necessarily decrease the inequity in mobility among different population groups.

Figure 6. Change in Accessibility, Build versus No-Build

Source: Zhang, Shen, and Sussman (1998).

Other Policy Impacts

The research team also examined the impacts of two additional regional policies:

Systemwide transit service improvements by mode (represented by a 10 percent decrease in travel time for each mode); and

Land use changes, specifically, increased development in rail station area zones.

Transit service improvements. A 10 percent reduction in zonal travel times by rail was found to have the greatest effect on job accessibility, raising the aggregate score from 1,073 to 1,196. Publico improvements had similar results. Compared to rail improvements, however, Publico improvements had the effect of decreasing rather than increasing the difference in accessibility indices between the lowest and highest income groups. Improvements to the bus service had only a small effect, due to the relatively small geographic coverage of the bus system and low operating speeds. The authors note also that there are institutional barriers to improving Publico service, while the barriers to increasing rail operating speeds are technical.

Land use changes. The research team also examined the impact of clustering development in rail transit station areas. They did this by first identifying TAZs that fell completely or partially within a quarter-mile vacant developable land (as determined from the regional land use database) contained in these TAZs was summed. Two scenarios were created, in which this land was assumed to be developed at residential densities of 10 and 18 dwelling units per acre, respectively. Population was then reallocated to these TAZs and removed from other TAZs in proportion to the growth increment of each TAZ between 1990 and 2020.

All else being equal in the 2010 Tren Urbano Build Case, clustering new development in transit station zones resulted in an increase in transit accessibility levels from 1,073 to 1,124 or 1,165, for the 10 and 18 dwelling unit per acre scenarios, respectively. Accessibility increased for all five income groups. The effect of clustering employment as well as residential uses was not measured, but this would make an interesting additional comparison.

Station-Area Walkability

Figure 7 illustrates the Tren Urbano station areas of quarter-mile radius as well as the actual area that can be reached by a five-minute walk. For comparison, San Alfonso Station has a WI of 0.65, while Las Lomas Station has a WI of only 0.27.

Figure 7. Quarter-Mile Radius and Walking Paths, Tren Urbano Stations

Source: Zhang, Shen, and Sussman (1999).

Calculation of walkability in this manner can help evaluate the pedestrian-friendliness of planned station areas and can provide a basis for evaluating pedestrian improvements that may be desired to increase access.

Conclusions

Strengths

This case study illustrates how accessibility measures, derived from travel demand model output, can be used to measure the distribution of benefits of alternative transportation projects. Some of the advantages of the approach taken here include:

By working with data in a GIS environment, the spatial distribution of current accessibility levels as well as future changes in accessibility can be displayed.

Accessibility levels and changes can be associated with socioeconomic data available at the TAZ level (in this case, median income) to estimate how current accessibility and benefits may vary by socioeconomic group.

The use of a multimodal travel model means that accessibility can be compared between automobile and transit modes. This can provide further insight into the equity of alternative transportation investments.

Limitations

At the same time, the measures and methods employed here have some pitfalls and limitations:

First, it is not clear how to value the accessibility measures in decision-making. The measures provide an idea of the relative benefits of various alternatives, but accessibility benefits are not directly valued in comparison to project costs.

Second, the gravity-based accessibility measure does not have an intuitive interpretation, and its acceptability to planners and decision-makers has not been proven. While it has theoretical advantages over the threshold-based measure described in the Montgomery County case study, its interpretation is somewhat harder to explain.

Third, as noted by the authors, the analysis by income group does not account for the potential future redistribution of population groups. This is a problem common to all equity analyses based on projections, since shifts in the location of population by income level cannot easily be modeled. Furthermore, the transportation project itself may affect this distribution. A rail transit project that provides significant benefits to commuters, for example, may attract higher-income residents to station areas, thus reducing the relative benefits of the system to low-income households.

Fourth, the accessibility analysis does not account for potential "spatial mismatches" in housing and employment, for example, a lack of affordable housing near lower-paying jobs. A method of matching residents to jobs by occupational class is discussed in the Appendix to the San Francisco Bay Area case study.

The analysis of transit service improvements provides a "first cut" at looking at the benefits of improvements across modes. However, the cost and feasibility of implementing a 10 percent level-of-service increase may vary considerably. Additional work would be required to identify potential strategies, costs, and implementation issues.

Further Development

The station-area walkability analysis described in this paper complements the regional accessibility analysis. The walkability index can be used to estimate the actual population served by transit. Current estimation methods assume that population is uniformly distributed throughout a TAZ, and that everyone within a given radius has access to the transit station. The walkability index can be used to refine this estimate to account for street networks that are not conducive to transit access.

The amount of population served by transit (by TAZ) might be incorporated into travel demand models to improve the ability of these models to predict transit ridership. It is further conceivable that with a high-quality GIS street network and a parcel-level land use database, the number of people or jobs within a five- or 10-minute walk of stations could be measured, rather than estimated based on an assumed uniform distribution of population. An example of such an analysis for a proposed LRT system in Orlando, Florida is shown in Jaskiewicz and Russ (1998).

Ultimately, the integration of micro-scale analysis techniques with regional travel demand models should serve to improve the accuracy of travel demand forecasting and to improve the usefulness of the resulting accessibility measures. This has been done in cities such as Portland, OR, which include a "Pedestrian Environment Factor" in their travel demand model. For additional discussion of the integration of micro-scale design variables into travel models, see Douglas (2000).